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Letter recognition plays an important role in reading and follows different phases of processing, from early visual feature detection to the access of abstract letter representations. Deaf ASL–English bilinguals experience orthography in two forms: English letters and fingerspelling. However, the neurobiological nature of fingerspelling representations, and the relationship between the two orthographies, remains unexplored. We examined the temporal dynamics of single English letter and ASL fingerspelling font processing in an unmasked priming paradigm with centrally presented targets for 200 ms preceded by 100 ms primes. Event-related brain potentials were recorded while participants performed a probe detection task. Experiment 1 examined English letter-to-letter priming in deaf signers and hearing non-signers. We found that English letter recognition is similar for deaf and hearing readers, extending previous findings with hearing readers to unmasked presentations. Experiment 2 examined priming effects between English letters and ASL fingerspelling fonts in deaf signers only. We found that fingerspelling fonts primed both fingerspelling fonts and English letters, but English letters did not prime fingerspelling fonts, indicating a priming asymmetry between letters and fingerspelling fonts. We also found an N400-like priming effect when the primes were fingerspelling fonts which might reflect strategic access to the lexical names of letters. The studies suggest that deaf ASL–English bilinguals process English letters and ASL fingerspelling differently and that the two systems may have distinct neural representations. However, the fact that fingerspelling fonts can prime English letters suggests that the two orthographies may share abstract representations to some extent. 

The lexical quality hypothesis proposes that the quality of phonological, orthographic, and semantic representations impacts reading comprehension. In Study 1, we evaluated the contributions of lexical quality to reading comprehension in 97 deaf and 98 hearing adults matched for reading ability. While phonological awareness was a strong predictor for hearing readers, for deaf readers, orthographic precision and semantic knowledge, not phonology, predicted reading comprehension (assessed by two different tests). For deaf readers, the architecture of the reading system adapts by shifting reliance from (coarse-grained) phonological representations to high-quality orthographic and semantic representations. In Study 2, we examined the contribution of American Sign Language (ASL) variables to reading comprehension in 83 deaf adults. Fingerspelling (FS) and ASL comprehension skills predicted reading comprehension. We suggest that FS might reinforce orthographic-to-semantic mappings and that sign language comprehension may serve as a linguistic basis for the development of skilled reading in deaf signers.

 

Picture-naming tasks provide critical data for theories of lexical representation and retrieval and have been performed successfully in sign languages. However, the specific influences of lexical or phonological factors and stimulus properties on sign retrieval are poorly understood. To examine lexical retrieval in American Sign Language (ASL), we conducted a timed picture-naming study using 524 pictures (272 objects and 251 actions). We also compared ASL naming with previous data for spoken English for a subset of 425 pictures. Deaf ASL signers named object pictures faster and more consistently than action pictures, as previously reported for English speakers. Lexical frequency, iconicity, better name agreement, and lower phonological complexity each facilitated naming reaction times (RT)s. RTs were also faster for pictures named with shorter signs (measured by average response duration). Target name agreement was higher for pictures with more iconic and shorter ASL names. The visual complexity of pictures slowed RTs and decreased target name agreement. RTs and target name agreement were correlated for ASL and English, but agreement was lower for ASL, possibly due to the English bias of the pictures. RTs were faster for ASL, which we attributed to a smaller lexicon. Overall, the results suggest that models of lexical retrieval developed for spoken languages can be adopted for signed languages, with the exception that iconicity should be included as a factor. The open-source picture-naming data set for ASL serves as an important, first-of-its-kind resource for researchers, educators, or clinicians for a variety of research, instructional, or assessment purposes. 

Abstract ASL-LEX is a publicly available, large-scale lexical database for American Sign Language (ASL). We report on the expanded database (ASL-LEX 2.0) that contains 2,723 ASL signs. For each sign, ASL-LEX now includes a more detailed phonological description, phonological density and complexity measures, frequency ratings (from deaf signers), iconicity ratings (from hearing non-signers and deaf signers), transparency (“guessability”) ratings (from non-signers), sign and videoclip durations, lexical class, and more. We document the steps used to create ASL-LEX 2.0 and describe the distributional characteristics for sign properties across the lexicon and examine the relationships among lexical and phonological properties of signs. Correlation analyses revealed that frequent signs were less iconic and phonologically simpler than infrequent signs and iconic signs tended to be phonologically simpler than less iconic signs. The complete ASL-LEX dataset and supplementary materials are available at https://osf.io/zpha4/ and an interactive visualization of the entire lexicon can be accessed on the ASL-LEX page: http://asl-lex.org/. 

abstract A growing body of research shows that both signed and spoken languages display regular patterns of iconicity in their vocabularies. We compared iconicity in the lexicons of American Sign Language (ASL) and English by combining previously collected ratings of ASL signs (Caselli, Sevcikova Sehyr, Cohen-Goldberg, & Emmorey, 2017) and English words (Winter, Perlman, Perry, & Lupyan, 2017) with the use of data-driven semantic vectors derived from English. Our analyses show that models of spoken language lexical semantics drawn from large text corpora can be useful for predicting the iconicity of signs as well as words. Compared to English, ASL has a greater number of regions of semantic space with concentrations of highly iconic vocabulary. There was an overall negative relationship between semantic density and the iconicity of both English words and ASL signs. This negative relationship disappeared for highly iconic signs, suggesting that iconic forms may be more easily discriminable in ASL than in English. Our findings contribute to an increasingly detailed picture of how iconicity is distributed across different languages.